Assessing the effectiveness of foraging radius models for seabird distributions using biotelemetry and survey data

Abstract

Relatively simple foraging radius models have the potential to generate predictive distributions for a large number of species rapidly, thus providing a cost-effective alternative to large-scale surveys or complex modelling approaches. Their effectiveness, however, remains largely untested. Here we compare foraging radius distribution models for all breeding seabirds in Ireland, to distributions of empirical data collected from tracking studies and aerial surveys. At the local/colony level, we compared foraging radius distributions to GPS tracking data from seabirds with short (Atlantic puffin Fratercula arctica, and razorbill Alca torda) and long (Manx shearwater Puffinus puffinus, and European storm-petrel Hydrobates pelagicus) foraging ranges. At the regional/national level, we compared foraging radius distributions to extensive aerial surveys conducted over a two-year period. Foraging radius distributions were significantly positively correlated with tracking data for all species except Manx shearwater. Correlations between foraging radius distributions and aerial survey data were also significant, but generally weaker than those for tracking data. Correlations between foraging radius distributions and aerial survey data were benchmarked against generalised additive models (GAMs) of the aerial survey data that included a range of environmental covariates. While GAM distributions had slightly higher correlations with aerial survey data, the results highlight that the foraging radius approach can be a useful and pragmatic approach for assessing breeding distributions for many seabird species. The approach is likely to have acceptable utility in complex, temporally variable ecosystems and when logistic and financial resources are limited.

Using foraging range and colony size to assess the vulnerability of breeding seabirds to oil across regions lacking at-sea distribution data

“…Another consideration is that the foraging ranges of species and individuals can be colony-specific, and be influenced by colony size (Patterson et al 2022), the density of conspecifics and other species within the region, and local food availability, as well as habitat features and dynamic environmental conditions that species might target for foraging (Wakefield et al 2017, Critchley et al 2019). For many colonies in the eastern North Atlantic, specific foraging ranges, and details on influencing environmental conditions, are currently unknown.…”

Marine Important Bird and Biodiversity Areas for Penguins in Antarctica, Targets for Conservation Action

“…Therefore, alternate solutions to direct observation of distribution patterns, such as atsea surveys or individual level tracking (e.g., Carroll et al, 2019), are required. Several methodologies for estimating seabird at-sea distribution in un-sampled regions have been proposed (Franklin, 2010;Grecian et al, 2012;Thaxter et al, 2012;Grimm et al, 2016;Soanes et al, 2016;Wakefield et al, 2017;Warwick-Evans et al, 2017Zhang et al, 2017;Critchley et al, 2018Critchley et al, , 2019Dias et al, 2018b; Supplementary Appendix: Determining at-sea distribution). Determining which method is most appropriate to derive species at-sea distributions should be considered in the context of available data for a given species and its typical foraging ecology (Cleasby et al, 2018;Oppel et al, 2018;Bolton et al, 2019).…”

“…Determining which method is most appropriate to derive species at-sea distributions should be considered in the context of available data for a given species and its typical foraging ecology (Cleasby et al, 2018;Oppel et al, 2018;Bolton et al, 2019). For relatively short ranging speciessuch as penguins during the breeding period (Oppel et al, 2018) -recent evidence shows that the foraging radius approach with a decay function coupled to population estimates can be a useful and pragmatic approach for assessing distributions; particularly in data sparse regions where studies involving the direct tracking of species has been limited (Critchley et al, 2018(Critchley et al, , 2019. This method involves estimating the distribution of seabirds from a source colony out to a specified buffer distance, and gives preferential weighting to those cells closest to the colony.…”

Progressing delineations of key biodiversity areas for seabirds, and their application to management of coastal seas

“…In marine environments, this challenge is particularly apparent when there is limited data detailing species distributions at sea. Several methodologies for estimating seabird at-sea distribution in un-sampled regions have been proposed (Critchley et al, 2018(Critchley et al, , 2019Dias et al, 2018;Franklin, 2010;Grecian et al, 2012;Grimm et al, 2016;Soanes et al, 2016;Thaxter et al, 2012;Wakefield et al, 2017;Warwick-Evans et al, 2017Zhang et al, 2017).…”

“…In marine environments, this challenge is particularly apparent when there is limited data detailing species distributions at sea. Several methodologies for estimating seabird at‐sea distribution in un‐sampled regions have been proposed (Critchley et al, 2018, 2019; Dias et al, 2018; Franklin, 2010; Grecian et al, 2012; Grimm et al, 2016; Soanes et al, 2016; Thaxter et al, 2012; Wakefield et al, 2017; Warwick‐Evans et al, 2017, 2018; Zhang et al, 2017). Yet, determining which method is most appropriate to derive species at‐sea distributions should be considered in the context of available data for a given species and its typical foraging ecology (Bolton et al, 2019; Cleasby et al, 2018; Oppel et al, 2018).…”